Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches.     

Representation of cancer in the medical literature--a bibliometric analysis

Background

There exists a lack of knowledge regarding the quantity and quality of scientific yield in relation to individual cancer types. We aimed to measure the proportion, quality and relevance of oncology-related articles, and to relate this output to their associated disease burden. By incorporating the impact factor(IF) and Eigenfactor™(EF) into our analysis we also assessed the relationship between these indices and the output under study.

Methods

All publications in 2007 were retrieved for the 26 most common cancers. The top 20 journals ranked by IF and EF in general medicine and oncology, and the presence of each malignancy within these titles was analysed. Journals publishing most prolifically on each cancer were identified and their impact assessed.

Principal Findings

63260 (PubMed) and 126845 (WoS) entries were generated, respectively. 26 neoplasms accounted for 25% of total output from the top medical publications. 5 cancers dominated the first quartile of output in the top oncology journals; breast, prostate, lung, and intestinal cancer, and leukaemia. Journals associated with these cancers were associated with much higher IFs and EFs than those journals associated with the other cancer types under study, although these measures were not equivalent across all sub-specialties. In addition, yield on each cancer was related to its disease burden as measured by its incidence and prevalence.

Conclusions

Oncology enjoys disproportionate representation in the more prestigious medical journals. 5 cancers dominate yield, although this attention is justified given their associated disease burden. The commonly used IF and the recently introduced EF do not correlate in the assessment of the preeminent oncology journals, nor at the level of individual malignancies; there is a need to delineate between proxy measures of quality and the relevance of output when assessing its merit. These results raise significant questions regarding the best method of assessment of research and scientific output in the field of oncology.     

Genetic Analysis of Central Carbon Metabolism Unveils an Amino Acid Substitution That Alters Maize NAD-Dependent Isocitrate Dehydrogenase Activity

Background

Central carbon metabolism (CCM) is a fundamental component of life. The participating genes and enzymes are thought to be structurally and functionally conserved across and within species. Association mapping utilizes a rich history of mutation and recombination to achieve high resolution mapping. Therefore, applying association mapping in maize (Zea mays ssp. mays), the most diverse model crop species, to study the genetics of CCM is a particularly attractive system.

Methodology/Principal Findings

We used a maize diversity panel to test the CCM functional conservation. We found heritable variation in enzyme activity for every enzyme tested. One of these enzymes was the NAD-dependent isocitrate dehydrogenase (IDH, E.C. 1.1.1.41), in which we identified a novel amino-acid substitution in a phylogenetically conserved site. Using candidate gene association mapping, we identified that this non-synonymous polymorphism was associated with IDH activity variation. The proposed mechanism for the IDH activity variation includes additional components regulating protein level. With the comparison of sequences from maize and teosinte (Zea mays ssp. Parviglumis), the maize wild ancestor, we found that some CCM genes had also been targeted for selection during maize domestication.

Conclusions/Significance

Our results demonstrate the efficacy of association mapping for dissecting natural variation in primary metabolic pathways. The considerable genetic diversity observed in maize CCM genes underlies heritable phenotypic variation in enzyme activities and can be useful to identify putative functional sites.     

Mechanisms of TNF induction by heat-killed Staphylococcus aureus differ upon the origin of mononuclear phagocytes

Mononuclear phagocytes are among the first immune cells activated after pathogens invasion. Although they all derive from the same progenitor in the bone marrow, their characteristics differ on the compartment from which they are derived. In this work, we investigated the contribution of phagocytosis for tumor necrosis factor (TNF) production by murine mononuclear phagocytes (monocytes, peritoneal and alveolar macrophages) in response to heat-killed Staphylococcus aureus (HKSA). Mononuclear phagocytes behaved differently, depending on their compartment of residence. Indeed, when bacterial uptake or phagosome maturation was blocked, activation through membrane receptors was sufficient for a maximal production of TNF and interleukin-10 by peritoneal macrophages. In contrast, monocytes, and to a lesser extent alveolar macrophages, required phagocytosis for optimal cytokine production. While investigating the different actors of signalization, we found that p38 kinase and phosphatidylinositol 3-kinase were playing an important role in HKSA phagocytosis and TNF production. Furthermore, blocking the α(5)β(1)-integrin significantly decreased TNF production in response to HKSA in all three cell types. Finally, using mononuclear phagocytes from NOD2 knockout mice, we observed that TNF production in response to HKSA was dependent on NOD2 for monocytes and peritoneal macrophages. In conclusion, we demonstrate that the mechanisms of activation leading to TNF production in response to HKSA are specific for each mononuclear phagocyte population and involve different recognition processes and signaling pathways. The influence of the compartments on cell properties and behavior should be taken into account, to better understand cell physiology and host-pathogen interaction, and to define efficient strategies to fight infection.     

Uncoupling charge movement from channel opening in voltage-gated potassium channels by ruthenium complexes

The Kv2.1 channel generates a delayed-rectifier current in neurons and is responsible for modulation of neuronal spike frequency and membrane repolarization in pancreatic β-cells and cardiomyocytes. As with other tetrameric voltage-activated K(+)-channels, it has been proposed that each of the four Kv2.1 voltage-sensing domains activates independently upon depolarization, leading to a final concerted transition that causes channel opening. The mechanism by which voltage-sensor activation is coupled to the gating of the pore is still not understood. Here we show that the carbon-monoxide releasing molecule 2 (CORM-2) is an allosteric inhibitor of the Kv2.1 channel and that its inhibitory properties derive from the CORM-2 ability to largely reduce the voltage dependence of the opening transition, uncoupling voltage-sensor activation from the concerted opening transition. We additionally demonstrate that CORM-2 modulates Shaker K(+)-channels in a similar manner. Our data suggest that the mechanism of inhibition by CORM-2 may be common to voltage-activated channels and that this compound should be a useful tool for understanding the mechanisms of electromechanical coupling.     

Synthesis and biological evaluation of five-membered heterocycles fused to cyclopenta[c]thiophene as new antitumor agents

A series of 10 derivatives 2-6 issued from the fusion of various five-membered heterocycles to cyclopenta[c]thiophene were evaluated for potential anticancer activity in the NCI's in vitro human disease-oriented tumor cell line screening panel that consisted of 60 human tumor cell lines arranged in nine subpanels, representing diverse histologies. Among these tested compounds, four were found to be cytotoxic allowing us to point out some structure-activity relationships. The oxazolidinone derivatives 2a-c displayed further in vivo antitumor activity in the hollow fiber assay and standard xenograft testing developed at the NCI.